17 citations · 38 across the 17 of their papers we have counts for
17 papers
BIMScript: Material-Aware Structured Scene Programs for BIM Ingestion
Prakash Kondibhau Naikade, Thomas B. Moeslund, Andreas Møgelmose
Structured-language models such as SceneScript reconstruct a scene as a short program of parametric commands, an inherently editable and semantically explicit representation. We as…
HSA: Hierarchical Slot Attention for Multi-granularity Scene-Decomposition
Neelu Madan, Rongzhen Zhao, Andreas Mogelmose +4
Slot attention is a powerful framework for object-centric learning, decomposing visual scenes into latent slots through iterative competitive attention. However, existing methods s…
A Hyperbolic Perspective on Hierarchical Structure in Object-Centric Scene Representations
Neelu Madan, Àlex Pujol, Andreas Møgelmose +4
Slot attention has emerged as a powerful framework for unsupervised object-centric learning, decomposing visual scenes into a small set of compact vector representations called \em…
SlotMatch: Distilling Object-Centric Representations for Unsupervised Video Segmentation
Diana-Nicoleta Grigore, Neelu Madan, Andreas Mogelmose +2
Unsupervised video segmentation is a challenging computer vision task, especially due to the lack of supervisory signals coupled with the complexity of visual scenes. To overcome t…
Towards a Multi-Agent Vision-Language System for Zero-Shot Novel Hazardous Object Detection for Autonomous Driving Safety
Shashank Shriram, Srinivasa Perisetla, Aryan Keskar +4
Detecting anomalous hazards in visual data, particularly in video streams, is a critical challenge in autonomous driving. Existing models often struggle with unpredictable, out-of-…
Can Vision-Language Models Understand and Interpret Dynamic Gestures from Pedestrians? Pilot Datasets and Exploration Towards Instructive Nonverbal Commands for Cooperative Autonomous Vehicles
Tonko E. W. Bossen, Andreas Møgelmose, Ross Greer
In autonomous driving, it is crucial to correctly interpret traffic gestures (TGs), such as those of an authority figure providing orders or instructions, or a pedestrian signaling…
Multimodal classification of forest biodiversity potential from 2D orthophotos and 3D airborne laser scanning point clouds
Simon B. Jensen, Stefan Oehmcke, Andreas Møgelmose +4
Assessment of forest biodiversity is crucial for ecosystem management and conservation. While traditional field surveys provide high-quality assessments, they are labor-intensive a…
Foundation Models for Video Understanding: A Survey
Neelu Madan, Andreas Moegelmose, Rajat Modi +2
Video Foundation Models (ViFMs) aim to learn a general-purpose representation for various video understanding tasks. Leveraging large-scale datasets and powerful models, ViFMs achi…
Driver Activity Classification Using Generalizable Representations from Vision-Language Models
Ross Greer, Mathias Viborg Andersen, Andreas Møgelmose +1
Driver activity classification is crucial for ensuring road safety, with applications ranging from driver assistance systems to autonomous vehicle control transitions. In this pape…
OpenTrench3D: A Photogrammetric 3D Point Cloud Dataset for Semantic Segmentation of Underground Utilities
Lasse H. Hansen, Simon B. Jensen, Mark P. Philipsen +3
Identifying and classifying underground utilities is an important task for efficient and effective urban planning and infrastructure maintenance. We present OpenTrench3D, a novel a…
Language-Driven Active Learning for Diverse Open-Set 3D Object Detection
Ross Greer, Bjørk Antoniussen, Andreas Møgelmose +1
Object detection is crucial for ensuring safe autonomous driving. However, data-driven approaches face challenges when encountering minority or novel objects in the 3D driving scen…
Raw Instinct: Trust Your Classifiers and Skip the Conversion
Christos Kantas, Bjørk Antoniussen, Mathias V. Andersen +6
Using RAW-images in computer vision problems is surprisingly underexplored considering that converting from RAW to RGB does not introduce any new capture information. In this paper…
Learning to Find Missing Video Frames with Synthetic Data Augmentation: A General Framework and Application in Generating Thermal Images Using RGB Cameras
Mathias Viborg Andersen, Ross Greer, Andreas Møgelmose +1
Advanced Driver Assistance Systems (ADAS) in intelligent vehicles rely on accurate driver perception within the vehicle cabin, often leveraging a combination of sensing modalities.…
ActiveAnno3D -- An Active Learning Framework for Multi-Modal 3D Object Detection
Ahmed Ghita, Bjørk Antoniussen, Walter Zimmer +5
The curation of large-scale datasets is still costly and requires much time and resources. Data is often manually labeled, and the challenge of creating high-quality datasets remai…
The Why, When, and How to Use Active Learning in Large-Data-Driven 3D Object Detection for Safe Autonomous Driving: An Empirical Exploration
Ross Greer, Bjørk Antoniussen, Mathias V. Andersen +2
Active learning strategies for 3D object detection in autonomous driving datasets may help to address challenges of data imbalance, redundancy, and high-dimensional data. We demons…
From CAD models to soft point cloud labels: An automatic annotation pipeline for cheaply supervised 3D semantic segmentation
Galadrielle Humblot-Renaux, Simon Buus Jensen, Andreas Møgelmose
We propose a fully automatic annotation scheme that takes a raw 3D point cloud with a set of fitted CAD models as input and outputs convincing point-wise labels that can be used as…
The AAU Multimodal Annotation Toolboxes: Annotating Objects in Images and Videos
Chris H. Bahnsen, Andreas Møgelmose, Thomas B. Moeslund
This tech report gives an introduction to two annotation toolboxes that enable the creation of pixel and polygon-based masks as well as bounding boxes around objects of interest. B…